Abstract:
The outcome of the research on possibility to non-invasively estimate systolic blood pressure is presented. The estimating was performed by applying machine learning techniques to the data acquired with the cardiac monitor CardioQvark. The developed in Russia cardiac monitor represents a portable device capable of registering synchronous electrocardiogram and photoplethysmogram. The presented results confirm the possibility of constructing algorithms capable of estimating systolic blood pressure of individual patients. Also the possibility to construct general purpose algorithms, i.e. algorithms capable of estimating blood pressure of any patient without additional setup, was confirmed.
Citation:
O. V. Senko, V. Ya. Chuchupal, A. A. Dokukin, “Non-invasive arterial pressure estimating with the cardiac monitor CardioQvark”, Mat. Biolog. Bioinform., 12:2 (2017), 536–545
This publication is cited in the following 4 articles:
A. A. Dokukin, O. V. Senko, “NOVYI DVUKhUROVNEVYI METOD MAShINNOGO OBUChENIYa DLYa OTsENIVANIYa VESchESTVENNYKh KhARAKTERISTIK OB'EKTOV”, Izvestiya Rossiiskoi akademii nauk. Teoriya i sistemy upravleniya, 2023, no. 4, 17
A. A. Dokukin, O. V. Sen'ko, “New Two-Level Machine Learning Method for Evaluating the Real Characteristics of Objects”, J. Comput. Syst. Sci. Int., 62:4 (2023), 619
D. Carolina Martinez-Reyes, “Algorithm for the joint analysis of the ECG and PPG signals”, UIS Ing., 20:4 (2021), 45–58
Yu. I. Zhuravlev, V. V. Ryazanov, O. V. Sen'ko, A. A. Dokukin, P. A. Afanas'ev, “On Some Transformations of Features in Machine Learning in Medicine”, Pattern Recognit. Image Anal., 28:4 (2018), 720